Battery state-of-health monitoring system and method

ABSTRACT

A battery state-of-health monitoring and prognosis method includes training off-line parity-relation parameters between extracted battery voltage and current signals during off-line battery discharge events using at least one good off-line battery. Portions of terminal voltage and current signals of an on-board battery corresponding to an on-board engine cranking process are extracted, and battery voltage of the on-board battery are estimated based on the parity-relation parameters and the extracted portions of the on-board battery current signals. A diagnostic residual defining a deviation between the battery voltage estimation of the on-board battery and extracted portions of the on-board battery terminal voltage signals is generated. A measure of battery state-of-health based on the diagnostic residual is then provided.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of application Ser. No. 12/107,323,filed Apr. 22, 2008, which is incorporated herein by reference.

TECHNICAL FIELD

This invention is related to vehicle battery monitoring systems.

BACKGROUND

The number of electrical devices in modern vehicles has been rapidlyincreasing. The vehicle electric power system is required to supplysufficient power to all such devices, including safety related systemsand convenience and entertainment systems. An electric power managementsystem balances the power demanded and the power provided to ensure thevehicle's start-up ability. An accurate and reliable knowledge of thebattery state is therefore desirable for effective electric powermanagement.

SUMMARY

A battery state-of-health monitoring and prognosis method includestraining off-line parity-relation parameters between extracted batteryvoltage and current signals during off-line battery discharge eventsusing at least one good off-line battery. Portions of terminal voltageand current signals of an on-board battery corresponding to an on-boardengine cranking process are extracted, and battery voltage of theon-board battery are estimated based on the parity-relation parametersand the extracted portions of the on-board battery current signals. Adiagnostic residual defining a deviation between the battery voltageestimation of the on-board battery and extracted portions of theon-board battery terminal voltage signals is generated. A measure ofbattery state-of-health based on the diagnostic residual is thenprovided.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments will now be described, by way of example, withreference to the accompanying drawings, in which:

FIG. 1 is a schematic diagram of a parity-relation based battery SOHmonitoring and prognosis method in accordance with the presentdisclosure;

FIG. 2 is a flowchart illustrating the procedure of parity-relationtraining in accordance with the present disclosure;

FIG. 3 is a flowchart illustrating the preprocessing procedure toextract the portion of battery signals corresponding to engine crankingin accordance with the present disclosure;

FIG. 4 is a graphical illustration of typical battery current andterminal voltage signals during engine cranking in accordance with thepresent disclosure;

FIG. 5 illustrates the data used to train the parity-relation. The datais shown via a plot of battery terminal voltage versus cranking currentin accordance with the present disclosure;

FIG. 6 is a flowchart illustrating the on-board implementation of theSOH evaluation and prognosis method in accordance with the presentdisclosure;

FIG. 7 is a graphical illustration showing a trend of the diagnosticresidual with respect to battery aging in accordance with the presentdisclosure; and

FIG. 8 illustrates advantages of the SOH monitoring system and method ofthe present invention by comparing a generated diagnostic residual ofthe instant invention and a residual generated by known resistance-basedmethods in accordance with the present disclosure.

DETAILED DESCRIPTION

The battery state-of-health (SOH) monitoring system and method inaccordance with the present disclosure uses a parity-relation baseddiagnostic residual that combines SOH information corresponding tobattery resistance and voltage loss during cranking to enhance batterydiagnostic and prognostic performance.

The battery SOH monitoring system and method disclosed herein does notrequire a physical battery model or real-time identification ofSOH-related battery model parameters and thus provides computationalefficiency over known model-based battery SOH monitoring systems.

The battery SOH monitoring system and method uses parity-relation basedbattery diagnosis and prognosis to provide pre-warning of batteryend-of-life, to help avoid unnecessary replacement of batteries, toreduce incidents of no-trouble-found and associated warranty costs, andto enhance electric power management in a vehicle environment.

The disclosure provides a parity-relation based data-driven approach tobattery SOH monitoring and prognosis. A diagnostic residual is designedto implicitly combine a plurality of battery SOH information determinedduring engine cranking, including parameters such as battery voltageloss and internal resistance. Combined SOH information is used toimprove the accuracy and robustness of the battery diagnosis andprognosis. The SOH information includes binary battery state-of-health(SOH) diagnosis (good or bad) and % level based prognosis duringcranking. Cranking data collected using known good batteries are used totrain a linear parity-relation between battery voltage and current.

FIG. 1 illustrates a schematic diagram of a battery SOH monitoringsystem 20 using a parity-relation based battery SOH monitoring andprognosis routine (24). The system 20 includes a plurality of modulesthat cooperate to process input signals received from a battery on-boarda vehicle to determine the battery's SOH. As used herein the term“module” or “modules” is defined as one or more units capable ofprocessing or evaluating signals input into or stored within the batterySOH monitoring system 20. Each module may be a stand-alone unit or aplurality of units comprising hardware or software or a combinationthereof.

More particularly, in an embodiment in accordance with the disclosure,the battery SOH monitoring system 20 resides on-board a vehicle andincludes a signal preprocessing module 26 adapted to receive a pluralityof inputs from a plurality of sensors 30A, 30B that sense parameters ofat least one battery 28. A battery voltage sensor 30A monitors battery28 terminal voltage and provides a battery terminal voltage signal 32Ato signal preprocessing module 26. Similarly, a battery current sensor30B monitors battery 28 current and provides a battery current signal32B to signal preprocessing module 26. Battery temperature signals 32C,and battery state-of-charge signals 32D are also input to battery SOHmonitoring system 20 as further described herein below with respect to aresidual evaluation module 40. Signal preprocessing module 26 is adaptedto provide an extracted battery voltage signal (V) corresponding to theengine cranking process to residual generation module 38 and anextracted battery current signal (I) corresponding to the enginecranking process to a voltage estimation module 34. The voltageestimator module 34 uses a trained parity-relation adapted to receivecalibrated parameters 37 from a memory device 36. The voltage estimatormodule 34 provides a voltage output (V) to the residual generationmodule 38. The residual generation module 38 provides one or moreresidual value outputs to a residual evaluation module 40. The residualvalue outputs from the residual generation module 40 may be filteredusing an associated low pass filter 104. The residual evaluation module40 further receives a battery temperature signal 32C provided, forexample, from a battery temperature sensor (not shown) or inferred fromexisting vehicle information such as engine coolant temperature. Abattery state-of-charge signal 32D is also provided to the residualevaluation module 40 for example from correlated battery open circuitvoltage and battery SOC data. The residual evaluation module 40 isadapted to obtain a battery SOH and provide a battery SOH indicatorsignal 42.

FIG. 2 is a flowchart of a routine (44) of off-line training theparity-relation for battery SOH monitoring on-board a vehicle. That isto say, off-line training occurs as part of vehicle development andcalibration. Initially, one or more known good batteries capable ofdelivering a desired power to start a vehicle are selected (46). Next, apreprocessing process (54) is performed to collect battery terminalvoltage and current signals during an engine cranking event and to thenextract the battery terminal voltage and current signals correspondingto the interval of engine cranking. In this respect, an engine crankingevent may include actual engine cranking events or simulated enginecranking events wherein battery discharge events closely mimickingelectrical draws upon the battery. The signal preprocessing process (54)is described in more detail in FIG. 3.

The flowchart in FIG. 3 depicts the preprocessing process (54) used toextract a selected portion of battery signals 56, 58 (shown in FIG. 4)that correspond to engine cranking. With additional reference to FIG. 4,exemplary battery voltage and battery current signals, 56 and 58respectively, extracted during engine cranking are illustrated.

With continuing reference to FIGS. 2-4, a portion of the batteryterminal voltage and current signals corresponding to the period ofengine cranking are extracted using the signal preprocessing process(54). More particularly, the extracted portions of the battery terminalvoltage 32A and battery current signals 32B characterize battery signalsgenerated during engine cranking occurring during a time intervaldefined by instant T1 and instant T2. A first set of data points 60, 62corresponding to an initial voltage drop due to starter engagement atinstant T1 is identified (64).

In an embodiment in accordance with the disclosure, a second set of datapoints 65, 68 corresponding to a first occurrence of a current Iexceeding a predefined threshold after instant T1, at instant T2, isidentified (70). In one embodiment in accordance with the disclosure,the predefined threshold is −100 A. In another embodiment in accordancewith the disclosure, a battery voltage signal threshold can be used todefine instant T2. In another embodiment in accordance with thedisclosure, only a part of the voltage and current signals in [T1, T2]are considered. In an embodiment in accordance with the disclosure, thesignals corresponding to −300 A<I<−100 A may be used. Generally, thevoltage and current signals extracted are within a range sufficient tostart a vehicle.

A graphical illustration 78 of extracted cranking data of two known goodbatteries is shown in FIG. 5, where the battery terminal voltage definesthe y-axis 80 and is plotted versus battery current defining the x-axis82. This cranking data illustrates a linear relationship between thebattery terminal voltage and the battery current occurring during anengine cranking event. The cranking data of good batteries is used totrain a linear parity-relation, wherein the trained linearparity-relation is depicted as line 84 in FIG. 5. More specifically,parameters V ₀ and R _(b) are determined (48) in FIG. 2 using a leastsquares method based on the following equation (1):

V= V ₀ +I* R _(b), (I<0 for discharge)  (1)

wherein V _(o) represents the intercept voltage and R _(b) representsthe battery resistance. Equation 1 may be further re-written in the formof Equation (2):

θ=[ V ₀ R _(b)]^(T) , x=[1I] ^(T), and y=V=x ^(T)θ.  (2)

Standard parameter estimation methods (recursive or non-recursive) maybe used to estimate the unknown parameter vector θ.

In an embodiment, an estimation of the unknown parameter vector θ. canbe obtained using a linear least squares fit. Once V ₀ and R _(b) aredetermined (48) off-line using data collected from known good batteriesas shown in FIG. 2, the V ₀ and R _(b) values are saved or stored (50)as calibration parameters within the memory device 36 associated withthe voltage estimator module 34. Each of two parameters V ₀ and R _(b)can be saved as a calibration table of battery temperature and SOC.

An on-board implementation of the SOH monitoring and prognosis routine(86) is illustrated in the routine depicted in FIG. 6. First, toevaluate the SOH of any batteries, a decision is made if an enginecranking event has been requested (88). If no engine cranking event orrequest is made, then the routine ends. If an engine cranking request ismade (90), then battery terminal voltage and current signals from abattery on-board a vehicle are inputted (91) into the signalpreprocessing module 26 for preprocessing. The battery voltage andbattery current signals corresponding to the short period of enginecranking are extracted (92) preferably with the preprocessing process(54) described in more detail in FIG. 3 herein above.

The trained parity-relation parameters V ₀ and R _(b) stored withinmemory device 36, and the current I extracted during signalpreprocessing of the on-board vehicle battery 28 are input into thevoltage estimator module 34.

A voltage estimate, {circumflex over (V)}, of on-board batteries is thendetermined (96) using the saved trained linear parity-relationparameters of V ₀ and R _(b) as described with reference to FIG. 2herein above. Voltage estimate {circumflex over (V)}(t) at time t isdetermined as follows in Equation (3):

{circumflex over (V)}(t)= V ₀ +I(t)* R _(b)  (3)

The voltage estimate, {circumflex over (V)}(t), and the actual voltageat time t, V(t), measured and extracted during signal preprocessing (92)of the on-board vehicle battery 28 are input into the residualgeneration module 38.

A diagnostic residual parameter r(t) defined as a deviation between thevoltage estimate {circumflex over (V)}(t) and the actual voltage V(t) isthen generated (100) by using Equation (4):

r(t)={circumflex over (V)}(t)−V(t),  (4)

wherein {circumflex over (V)}(t) is the voltage estimate, and V(t) isthe actual battery terminal voltage of the on-board battery duringengine cranking. Thus, the residual r(t) represents a deviation betweenthe actual voltage measurement V(t) and the voltage estimate {circumflexover (V)}(t).

In an embodiment, to minimize the effect of noise, the on-boardimplementation of the SOH evaluation and prognosis routine (86) proceedsto filter a raw residual signal (102) by using a low-pass filter 104.

In an embodiment, an average of the diagnostic residual r(t) can be usedfor battery SOH monitoring for each cranking data set. Next, as shown inFIG. 6, both the battery temperature and open circuit voltage areobtained (108), from which a battery start-up SOC may be determined(110) by using a calibrated look-up table of battery temperature andbattery open circuit voltage. Then, a threshold on the diagnosticresidual, r, may be determined (112) from a look-up table of batterytemperature and start-up SOC.

FIG. 7 is a graphical illustration depicting the trend of the diagnosticresidual with respect to battery aging. Four batteries were aged fromfresh to dead through accelerated cycling. The cranking data wasperiodically collected during the aging process. As shown in FIG. 7, allthe residuals increase as a result of batteries aging.

Through some algebraic manipulations, it can be shown that the residualimplicitly combines battery SOH information provided by multiple SOHindicators which may include, but are not limited to, battery voltageloss and internal resistance during engine cranking. Hence diagnosticaccuracy and robustness may be improved. More specifically, the residualremains around zero for good batteries. As the battery ages, theresidual is seen to significantly increases due to increasing batteryvoltage loss and internal resistance.

Finally, the battery SOH may be determined by comparing the generatedresidual value to a predefined threshold (114). The comparisondetermines if the filtered residual value exceeds the threshold of thediagnostic residual (115). If the filtered residual exceeds thethreshold of the diagnostic residual (116), a warning message indicatinga bad battery may be provided (118). Otherwise, if the filtered residualdoes not exceed the threshold of the diagnostic residual (120), abattery SOH index is generated (122) by using Equation (5):

$\begin{matrix}{{{SOH}_{INDEX} = {\frac{r - \overset{\_}{r}}{r_{fresh} - \overset{\_}{r}}*100\%}},} & (5)\end{matrix}$

wherein r is the residual threshold, and r_(fresh) is the nominalresidual value for fresh and good batteries. When SOH<0, SOH is set to0%, and when SOH>1, SOH is set to 100%. The battery SOH characterizesthe battery remaining useful life (RUL).

Finally the battery SOH signal is output (124) from the battery SOHmonitoring system 20 to notify a user of the battery's SOH.

FIG. 8 is a graphical illustration of one aspect of the presentparity-relation based SOH monitoring system 20 routine (86). The solidcurve 130 represents the diagnostic residual generated by using thepresent disclosure. The dotted curve 132 corresponds to the residualgenerated by using a known resistance-based method. The presentdisclosure demonstrates improved fault sensitivity and robustness overknown methods.

The disclosure has described certain preferred embodiments andmodifications thereto. Further modifications and alterations may occurto others upon reading and understanding the specification. Therefore,it is intended that the disclosure not be limited to the particularembodiment(s) disclosed as the best mode contemplated for carrying outthis disclosure, but that the disclosure will include all embodimentsfalling within the scope of the appended claims.

1. An on-board battery state-of-health monitoring and prognosisapparatus, comprising: a signal preprocessing module for extractingportions of on-board battery terminal voltage and current signalscorresponding to an on-board engine cranking process; a memory storagedevice for recalling parity-relation parameters between battery voltageand current signals derived during off-line battery discharge eventscorresponding to at least one good off-line battery; a voltageestimation module for estimating battery voltage of said an on-boardbattery based on recalled parity-relation parameters and the extractedportions of the on-board battery current signals; a diagnostic residualgeneration module for generating a diagnostic residual defining adeviation between the estimated battery voltage of the on-board batteryand the extracted portion of the on-board battery terminal voltagesignals; and a residual evaluation module for providing a measure ofbattery state-of-health based on said diagnostic residual.
 2. Theapparatus of claim 1, wherein the diagnostic residual generation modulecomprises a low pass filter.
 3. An on-board vehicular batterystate-of-health monitoring and prognosis method comprising: determiningoff-line parameters of a parity-relation between extracted batterysignals using at least one good off-line battery; storing the off-linedetermined parity-relation parameters in a memory device associated witha voltage estimation module within a battery state-of-health monitoringsystem on-board a vehicle having an engine; inputting on-board batteryterminal voltage and current signals from an on-board battery into asignal preprocessor module within said battery state-of-healthmonitoring system when said engine is cranked; extracting a portion ofthe on-board battery terminal voltage and current signals using thesignal preprocessor module; estimating the battery voltage of theon-board battery using the stored off-line determined parity-relationparameters and the extracted portion of the on-board battery currentsignals; generating a diagnostic residual with a residual generationmodule, wherein the diagnostic residual represents a deviation betweenthe estimated battery voltage and the corresponding on-board batteryterminal voltage signals; and evaluating the diagnostic residual in aresidual evaluation module to determine a state-of-health metric of theon-board battery.